[Bioc-devel] parallel package generics

Hi,

With Florian use case, there seems to be a strong/immediate need for
dispatching on the cluster-like object passed as the 1st argument to
parLapply() and all the other functions in the parallel package that
belong to the "snow family" (14 functions in total, all documented in
?parallel::parLapply). So we've just added those 14 generics to
BiocGenerics 0.5.1. We're postponing the "multicore family" (i.e.
mclapply(), mcmapply(), and pvec()) for now.

Note that the 14 new generics dispatch at least on their 1st argument
('cl'), but also on their 2nd argument when this argument is 'x', 'X'
or 'seq' (expected to be a vector-like or matrix-like object). This
opens the door to defining methods that take advantage of the of the
implementation of particular vector-like or matrix-like objects.

Also note that, even if some of the 14 functions in the "snow family"
are simple convenience wrappers to other functions in the family, we've
made all of them generics. For example clusterEvalQ() is a simple
wrapper to clusterCall():

  > clusterEvalQ

  function (cl = NULL, expr)
  clusterCall(cl, eval, substitute(expr), env = .GlobalEnv)
  <environment: namespace:parallel>

And it seems (at least intuitively) that implementing a "clusterCall"
method for my cluster-like objects should be enough to have
clusterEvalQ() work out-of-the-box on those objects. But, sadly enough,
this is not the case:

  setClass("FakeCluster", representation(nnodes="integer"))

  setMethod("clusterCall", "FakeCluster",
      function (cl=NULL, fun, ...) fun(...)
  )

Then:

  > mycluster <- new("FakeCluster", nnodes=10L)
  > clusterCall(mycluster, print, 1:6)

  [1] 1 2 3 4 5 6

  > clusterEvalQ(mycluster, print(1:6))

  Error in checkCluster(cl) : not a valid cluster

This is because the "clusterEvalQ" default method is calling
parallel::clusterCall() (which is *not* the generic), instead of
calling BiocGenerics::clusterCall() (which *is* the generic).

This would be avoided if clusterCall() was a generic defined in
the parallel package itself (or in a package that parallel depends
on). And this would of course be a better solution than having those
generics in BiocGenerics. Is someone willing to bring that case to
R-devel?

In the mean time I need to define a "clusterEvalQ" method:

  setMethod("clusterEvalQ", "FakeCluster",
      function (cl=NULL, expr)
          clusterCall(cl, eval, substitute(expr), env=.GlobalEnv)
  )

And then:

  > clusterEvalQ(mycluster, print(1:6))

  [1] 1 2 3 4 5 6

Finally note that this method I defined for my objects could be made the
default "clusterEvalQ" method (i.e. the clusterEvalQ,ANY method) and we
could put it in BiocGenerics. Or, since there is apparently nothing to
win by having clusterEvalQ() being a generic in the first place, we
could redefine clusterEvalQ() as an ordinary function in BiocGenerics.
This function would be implemented *exactly* like
parallel::clusterEvalQ() (and it would mask it), except that now
it would call BiocGenerics::clusterCall() internally.

What should we do?

H.


On 10/24/2012 09:07 AM, Cook, Malcolm wrote:

On 10/24/12 12:44 AM, "Michael Lawrence" <lawrence.michael at gene.com>
wrote:

I agree that it would fruitful to have parLapply in BiocGenerics. It
looks
to be a flexible abstraction and its presence in the parallel package
makes
it ubiquitous. If it hasn't been done already, mclapply (and mcmapply)
would be good candidates, as well. The fork-based parallelism is
substantively different in terms of the API from the more general
parallelism of parLapply.

Someone was working on some more robust and convenient wrappers around
mclapply. Did that ever see the light of day?

If you are referring to

http://thread.gmane.org/gmane.science.biology.informatics.conductor/43660

in which I had offered some small changes to parallel::pvec

      https://gist.github.com/3757873/

and after which Martin had provided me with a route I have not (yet?)
followed toward submitting a patch to R for consideration by R-devel /
Simon Urbanek in


http://grokbase.com/t/r/bioc-devel/129rbmxp5b/applying-over-granges-and-o
th
er-vectors-of-ranges#201209248dcn0tpwt7k7g9zsjr4dha6f1c

On Tue, Oct 23, 2012 at 12:13 PM, Steve Lianoglou <
mailinglist.honeypot at gmail.com**> wrote:

  In response to a question from yesterday, I pointed someone to the

ShortRead `srapply` function and I wondered to myself why it had to
necessarily by "burried" in the ShortRead package (aside from it
having a `sr` prefix).

I don't know that srapply necessarily 'got it right'...

One thing I like about srapply is its support for a reduce argument.

I had thought it might be a good idea to move that (or something
like
that) to BiocGenerics (unless implementations aren't allowed there)
but also realized that it would add more dependencies where someone
might not necessarily need them.

But, almost surely, a large majority of the people will be happy to
do
some form of ||-ization, so in my mind it's not such an onerous
thing
to add -- on the other hand, this large majority is probably
enriched
for people who are doing NGS analysis, in which case, keeping it in
ShortRead can make some sense.

I remain confused about the need for putting any of this into
BiocGenerics
at all.  It seems to me that properly construed parallization primitives
ought to 'just work' with any object which supports indexing and length.

I would appreciate hearing arguments to the contrary.

Florian, in a similar vein, could we not seek to change
parallel::makeCluster to be extensible to, say, support SGE cluster?
THis
seems like the 'right thing to do'.  ???


Regardless, I think we have raised some considerations that might inform
improvements to parallel, including points about error handling,
reducing
results, block-level parallization over List/Vector (in addition to
vector), etc.

I think perhaps having a google doc that we can collectively edit to
contain the requirements we are trying to achieve might move us forward
effectively. Would this help? Or perhaps a page under
http://wiki.fhcrc.org/bioc/DeveloperPage/#discussions ???

Taking one step back, I recall some chatter last week (or two) about
some better ||-ization "primitives" -- something about a pvec
doo-dad,
and there being ideas to wrap different types of ||-ization behind
an
easy to use interface (I think this was the convo), and then I took
a
further step back and often wonder why we just don't bite the bullet
and take advantage of the `foreach` infrastructure that is already
out
there -- in which case, I could imagne a "doSGE" package that might
handle the particulars of what Florain is referring to. You could
then
configure it externally via some
`registerDoSGE(some.config.**object)`
and just have the package code happily run it through `foreach(...)
%dopar%` and be done w/ it.


  IMHO it is relevant.  I have not looked for other abstractions,
and
this

one seems
to work.  Florian's objectives might be a good test case for
adequacy.

The registerDoDah does seem to be a useful abstraction.

Is this not more-or-less the intention of parallel::setDefaultCluster?

--Malcolm

I think there's a lot of work to do for some sort of coordinated
parallelization that putting parLapply into BiocGenerics might
encourage;
not good things will happen when everyone in a call stack tries to
parallelize independently. But I'm in favor of parLapply in
BiocGenerics at
least for the moment.

Martin

  ... at least, I thought this is what was being talked about here
(and

popped up a week or two ago) -- sorry if I completely missed the
mark
...

-steve


On Tue, Oct 23, 2012 at 10:38 AM, Hahne, Florian
<florian.hahne at novartis.com> wrote:

Hi Martin,
I could define the generics in my own package, but that would mean
that
those will only be available there, or in the global environment
assuming
that I also export them, or in all additional packages that
explicitly
import them from my name space. Now there already are a whole bunch
of
packages around that all allow for parallelization via a cluster
object.
Obviously those all import the parLapply function from the parallel
package. That means that I can't simply supply my own modified
cluster
object, because the code that calls parLapply will not know about
the
generic in my package, even if it is attached. Ideally parLapply
would
be
a generic function already in the parallel package. Not sure who
needs
to
be convinced in order for this to happen, but my gut feeling was
that it
could be easier to have the generic in BiocGenerics.
Maybe I am missing something obvious here, but imo there is no way
to
overwrite parLapply globally for my own class unless the generic is
imported by everyone who wants to make use of the special method.
Florian
--






On 10/23/12 2:20 PM, "Martin Morgan" <mtmorgan at fhcrc.org> wrote:

  On 10/17/2012 05:45 AM, Hahne, Florian wrote:

Hi all,
I was wondering whether it would be possible to have proper
generics

for

some of the functions in the parallel package, e.g. parLapply and

clusterCall. The reason I am asking is because I want to build an
S4
class
that essentially looks like an S3 cluster object but knows how to
deal
with the SGE. That way I can abstract away all the overhead
regarding
job
submission, job status and reducing the results in the parLapply
method
of
that class, and would be able to supply this new cluster object
to
all
of
my existing functions that can be processed in parallel using a
cluster
object as input. I have played around with the BatchJobs package
as an
abstraction layer to SGE and that work nicely. As a test case I
have
created the necessary generics myself in order to supply my own
SGEcluster
object to a function that normally deals with the "regular"
parallel
package S3 cluster objects and everything just worked out of the
box,
but
obviously this fails once I am in a name space and my generic is
not
found
anymore. Of course what we would really want is some proper
abstraction
of
parallelization in R, but for now this seem to be at least a
cheap
compromise. Any thoughts on this?

Hi Florian -- we talked about this locally, but I guess we didn't
actually send
any email!

Is there an obstacle to promoting these to generics in your own
package?
The
usual motivation for inclusion in BiocGenerics has been to avoid
conflicts
between packages, but I'm not sure whether this is the case (yet)?
This
would
also add a dependency fairly deep in the hierarchy.

What do you think?

Martin

  Florian

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--
Steve Lianoglou
Graduate Student: Computational Systems Biology
   | Memorial Sloan-Kettering Cancer Center
   | Weill Medical College of Cornell University
Contact Info:

http://cbio.mskcc.org/~lianos/**contact<http://cbio.mskcc.org/%7Elian
os
/contact>

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--
Computational Biology / Fred Hutchinson Cancer Research Center
1100 Fairview Ave. N.
PO Box 19024 Seattle, WA 98109

Location: Arnold Building M1 B861
Phone: (206) 667-2793

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--
Herv? Pag?s

Program in Computational Biology
Division of Public Health Sciences
Fred Hutchinson Cancer Research Center
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